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arXiv 2025年

作者： Chen, Jiale Wang, Wei Shi, Chongyang Dong, Li Li, Yuanman Hu, Xiping School of Computer Science Beijing Institute of Technology Beijing100081 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China

Robust Reversible Watermarking (RRW) enables perfect recovery of cover images and watermarks in lossless channels while ensuring robust watermark extraction under lossy channels. However, existing RRW methods, mostly non-deep learning-based, suffer from complex designs, high computational costs, and poor robustness limiting their practical applications. To address these issues, this paper proposes Deep Robust Reversible Watermarking (DRRW), a deep learning-based RRW scheme. DRRW introduces an Integer Invertible Watermark Network (iIWN) to achieve an invertible mapping between integer data distributions, fundamentally addressing the limitations of conventional RRW approaches. Unlike traditional RRW methods requiring task-specific designs for different distortions, DRRW adopts an encoder-noise layer-decoder framework, enabling adaptive robustness against various distortions through end-to-end training. During inference, the cover image and watermark are mapped into an overflowed stego image and latent variables. Arithmetic coding efficiently compresses these into a compact bitstream, which is embedded via reversible data hiding to ensure lossless recovery of both the image and watermark. To reduce pixel overflow, we introduce an overflow penalty loss, significantly shortening the auxiliary bitstream while improving both robustness and stego image quality. Additionally, we propose an adaptive weight adjustment strategy that eliminates the need to manually preset the watermark loss weight, ensuring improved training stability and performance. Experiments on multiple datasets demonstrate that DRRW achieves notable performance advantages. Compared to state-of-the-art RRW methods, DRRW improves robustness and reduces embedding, extraction, and recovery complexities by 55.14×, 5.95×, and 3.57×, respectively. The auxiliary bitstream is shortened by 43.86×, and reversible embedding succeeds on 16,762 images in the PASCAL VOC 2012 dataset, marking a significant step toward pra

关键词： Image enhancement

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Prediction of Heat Energy Release Rate for Ammonia Combustion in a Constant Volume Combustion Chamber: A Machine Learning Approach 15

Prediction of Heat Energy Release Rate for Ammonia Combustio...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Fang, Lijia Singh, Hardeep Ichiyanagi, Mitsuhisa Suzuki, Takashi Raboaca, Maria Simona Filote, Constantin Verma, Chaman Sophia University Department of Engineering and Applied Sciences Tokyo Japan University of Windsor Turbulence and Energy Laboratory Windsor Canada Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Research and Development Ramnicu Valcea Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania Eötvös Loránd University Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350321388

Addressing global warming requires immediate and concerted action. Transitioning to non-conventional energy sources, such as solar and Aeolian power, reducing emissions from transportation and industry, and implementing sustainable land-use practices are crucial steps. Therefore, Ammonia attracts researchers when used as a fuel, and offers several potential benefits such as higher energy density. However, due to poor combustion speed of Ammonia offers slower energy release. To enhance the average flame velocity of Ammonia, the current investigation employed the exhaust gas expelled from the secondary chamber to compress the mixture within the primary chamber and facilitate its HCCI combustion. The main goal of research is the optimization on air-fuel ER to contribute to the development of more efficient and sustainable constant volume combustion. However, the time required for constructing physical prototypes and conducting experiments can be considerable, thus, to mitigate these challenges, the authors turned to machine learning tools as an alternative approach. In the proposed research, results were evaluated through Support Vector Regression. However, due to poor prediction accuracy, authors tried KNeighborsRegressor model for predicting and validating the results. Finally, it is revealed from the KNN model that data sets are highly accurate. © 2023 IEEE.

关键词： Ammonia

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Development Progress of SWLBM a Framework Based on Lattice Boltzmann Method for Fluid Dynamics Simulation 1

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18th International Symposium on Applied Reconfigurable computing, ARC 2022

作者： Xuesen, Chu Xiang, He Fang, Li Zhao, Liu Guangwen, Yang Department of Computer Science and Technology Tsinghua University Beijing100084 China China Ship Scientific Research Center Wuxi214082 China Taihu Laboratory of DeepSea Technological Science Wuxi214082 China Jiangnan Institute of Computing Technology Wuxi214083 China National Research Center of Parallel Computer Engineering and Technology Beijing100084 China National Supercomputing Center in Wuxi Wuxi214072 China

ISBN: (数字)9783031199837

ISBN: (纸本)9783031199820

SWLBM is a software framework based on Lattice Boltzmann Method (LBM) for Computational Fluid Dynamic (CFD) running on Sunway many-core processors. In this paper, we review the achievements of code developing in early stage and introduce the development progress recently of this software, including the development of parallel optimization for Sunway new-generation supercomputing system, functional extensions of the software like pre-process function for mesh generation from a geometry file with STL file and BMP file, Immersed boundary condition for moving subject simulation. Some applications with SWLBM will be introduced to show the advantage of this software over other CFD code in large-scale simulations. SWLBM is still under development, with the continuous improvement of functions, it will play a greater role in the field of fluid simulation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational fluid dynamics

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DESIGN AND DEVELOPMENT OF MORPHOLOGICAL ANALYZER FOR TIGRIGNA VERBS USING HYBRID APPROACH

Research Square

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research Square 2023年

作者： Gebremeskel, Hagos Gebremedhin Chong, Feng Heyan, Huang Beijing Engineering Research Center of High Volume Language Information Processing Cloud Computing Applications Laboratory School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science Mekelle University Tigray Mekelle231 Ethiopia

Morphological analyzer is the base for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs. © 2023, CC BY.

关键词： Machine learning

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Low-loss Cd-substituted Mg ferrites with matching impedance for high-frequency-range antennas

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Journal of Magnesium and Alloys 2021年第4期9卷 1403-1413页

作者： Gongwen Gan Dainan Zhang Jie Li Gang Wang Xin Huang Yan Yang Yiheng Rao Xueying Wang Huaiwu Zhang Ray T.Chen State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of ChinaNo.4Section 2North Jianshe RoadChengdu 610054China Microelectronic Research Center Department of Electrical and Computer EngineeringUniversity of Texas at AustinAustinTX 78758USA

The effects of Cd^(2+) ions on the microstructure,magnetic properties,and dielectric properties of Bi_(2)O_(3)-added MgFe_(2)O_(4) ferrites(Cd_(x)Mg_(1-x)Fe_(2)O_(4),x=0.00,0.15,0.30 and 0.45)are obtained by adopting the solid-state reaction method at a low temperature(910℃).The objective is to achieve matching impedances,low magnetic and dielectric losses(tanδμand tanδε,respectively),and a relatively large miniaturization factor to reduce antenna *** results indicate that the cations occupying the tetrahedral(A)and octahedral(B)ion sites are redistributed,resulting in an enhanced super-exchange interaction between the two *** a result,improved magnetization,including the increase in saturation magnetization(41.74 emu/g)and decrease in coercivity(63.75 Oe),is *** real part of permeability(μ')also increases with increasing concentration of Cd^(2+) *** x is 0.15,matching impedances with equivalent μ'and ε'values are obtained over a long frequency range(1–150MHz).Moreover,the formation of a dense microstructure guarantees that losses occur at low orders of magnitude(tanδμ≈10−2 and tanδε≈10−3).Accordingly,these properties afford wide application perspectives for the proposed compounds in the high-frequency region,i.e.,from high-frequency to very-high-frequency bands.

关键词： Cd-substituted Mg compounds Magnetic properties Dielectric properties Matching impedance Low loss High frequency antenna

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Evolutionary Reinforcement Learning via Cooperative Coevolution

arXiv

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arXiv 2024年

作者： Hu, Chengpeng Liu, Jialin Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong

Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient exploration. However, the poor scalability of genetic operators limits the efficiency of optimising high-dimensional neural networks. To address this issue, this paper proposes a novel cooperative coevolutionary reinforcement learning (CoERL) algorithm. Inspired by cooperative coevolution, CoERL periodically and adaptively decomposes the policy optimisation problem into multiple subproblems and evolves a population of neural networks for each of the subproblems. Instead of using genetic operators, CoERL directly searches for partial gradients to update the policy. Updating policy with partial gradients maintains consistency between the behaviour spaces of parents and offspring across generations. The experiences collected by the population are then used to improve the entire policy, which enhances the sampling efficiency. Experiments on six benchmark locomotion tasks demonstrate that CoERL outperforms seven state-of-the-art algorithms and baselines. Ablation study verifies the unique contribution of CoERL's core ingredients. Copyright © 2024, The Authors. All rights reserved.

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Johnsen-Rahbek Capstan Clutch: A High Torque Electrostatic Clutch

Johnsen-Rahbek Capstan Clutch: A High Torque Electrostatic C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Timothy E. Amish Jeffrey T. Auletta Chad C. Kessens Joshua R. Smith Jeffrey I. Lipton Dept of Electrical and Computer Engineering University of Washington Seattle WA USA US Army Research Directorate DEVCOM Army Research Laboratory MD USA Paul G Allen School of Computer Science and Engineering University of Washington Seattle WA USA Mechanical and Industrial Engineering Department Northeastern University Boston MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In many robotic systems, the holding state consumes power, limits operating time, and increases operating costs. Electrostatic clutches have the potential to improve robotic performance by generating holding torques with low power consumption. A key limitation of electrostatic clutches has been their low specific shear stresses which restrict generated holding torque, limiting many applications. Here we show how combining the Johnsen-Rahbek (JR) effect with the exponential tension scaling capstan effect can produce clutches with the highest specific shear stress in the literature. Our system generated 31.3 N/cm 2 sheer stress and a total holding torque of 7.1 N•m while consuming only 2.5 mW/cm 2 at 500 V. We demonstrate a theoretical model of an electrostatic adhesive capstan clutch and demonstrate how large angle (θ > 2π) designs increase efficiency over planar or small angle (θ < π) clutch designs. We also report the first unfilled polymeric material, polybenzimidazole (PBI), to exhibit the JR-effect.

关键词： Electric potential Torque Power demand Limiting Costs Polymers Electrostatics

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A transactional-behavior-based hierarchical gated network for credit card fraud detection

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Xie, Yu Zhou, MengChu Liu, Guanjun Wei, Lifei Zhu, Honghao De Meo, Pasquale College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Tongji University Shanghai201804 China College of Computer Science and Information Engineering Bengbu University Bengbu233030 China Department of Ancient and Modern Civilizations University of Messina Messina98166 Italy

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art. © 2025 Institute of electrical and Electronics Engineers Inc.. All right

关键词： Deep learning

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Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition

arXiv

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arXiv 2025年

作者： Zhang, Jie Ni, Yiyang Li, Jun Chen, Guangji Wang, Zhe Shi, Long Jin, Shi Chen, Wen Poor, H. Vincent School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China University Jiangsu Institute of Educational Science Research Nanjing210094 China National Mobile Communications Research Laboratory Southeast University China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Trajectory Design for 3D UAV Localization in UAV Based Networks

Trajectory Design for 3D UAV Localization in UAV Based Netwo...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and four passive UAVs. Each passive UAV receives signals transmitted from the active UAV and reflected by the target UAV, and then estimates the distance from the active UAV to the target UAV and then from the target UAV to the passive UAV. Each passive UAV then transmits this distance information to a base station (BS), which estimates the location of the target UAV. Since the target UAV will change its location according to its performed task, each controlled UAV must optimize its trajectory to continuously localize the target UAV. This trajectory design problem is formulated as an optimization problem whose goal is to jointly optimize the trajectories of active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards, thus finding better trajectories for controlled UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 58.3% and 84.8%, compared to VD-RL and independent DRL methods, respectively.

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